Economic studies of health care costs increasingly point to technological innovations as an important cause of expenditure growth. This research focuses on cardiac radionuclide scanning (CRS) as an example of an important new technology which is being diffused, modified, and utilized without evaluation of its contribution to coronary artery disease (CAD) diagnosis. The project will undertake to achieve three major objectives focused upon the prospective evaluation of CRS and other techniques used to diagnose CAD. The study's first objective will be to develop a clinical decision-making model, based upon knowledge of the diagnostic efficacy and costs of CRS. The model will be developed using decision analysis and will be expressed in decision tree format. The second objective of the study will be to develop a dynamic computer model to assess the effectiveness and cost-effectiveness of alternative diagnostic strategies of CAD, and to select the most efficient single test or test combination for each patient type, in advance of testing. This model will use Bayes' Theorem of Conditional Probability to relate the post-test probability of disease to patient characteristics, the sensitivity and specificity of alternate diagnostic tests, and varying levels of physician certainty thresholds required for further testing or treatment decisions. To assure maximum usefulness, the computer model will be subjected to sensitivity analysis and to predictive validation procedures. Finally, the project's third objective will be to develop public policy options regarding CRS based upon the previous analyses, and to assess potential implementation strategies.